Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Geomechanics and Engineering Volume 34, Number 5, September10 2023 , pages 507-527 DOI: https://doi.org/10.12989/gae.2023.34.5.507 |
|
|
|
Predicting the Young's modulus of frozen sand using machine learning approaches: State-of-the-art review |
||
Reza Sarkhani Benemaran and Mahzad Esmaeili-Falak
|
||
| Abstract | ||
| Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria. | ||
| Key Words | ||
| Artificial Ground Freezing; data mining; forecasting; laboratory test; numerical simulation | ||
| Address | ||
| Reza Sarkhani Benemaran: Department of Civil Engineering, Faculty of Geotechnical Engineering, University of Zanjan, Zanjan, Iran Mahzad Esmaeili-Falak: Department of Civil Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran | ||